Panel Data Research Center, Keio University
PDRC Discussion Paper Series

Relative income and life satisfaction: Who compares their income to whose
income and to what extent?
Kenichi Shiraishi, Kazuto Sumita, Kazuki Kamimura,
Shohei Okamoto, Kohei Komamura
14 October, 2019
DP2019-002
https://www.pdrc.keio.ac.jp/en/publications/dp/5500/

Panel Data Research Center, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
info@pdrc.keio.ac.jp
14 October, 2019

Relative income and life satisfaction: Who compares their income to whose income and to what extent?

Kenichi Shiraishi, Kazuto Sumita, Kazuki Kamimura, Shohei Okamoto, Kohei Komamura
PDRC Keio DP2019-002
14 October, 2019
JEL Classification: I31
Keywords: Life satisfaction;Fixed effects ordered logit model;Inverse of distance;Relative
income;Reference group

Abstract
Relative income is considered a key to explain the paradox that, since the end of the Second World War, an
increase in per capita income does not raise the average happiness or life satisfaction in western countries andJapan. This study uses comparison income as the measure of the relative income and verifies the sign of thecoefficient of comparison income in terms of life satisfaction by conducting micro-econometric analysis. This
study offers three main contributions. The first is to estimate the life satisfaction equation by using the fixed
effects ordered logit model, which previous studies rarely consider. Second, to estimate the average income of thereference group, we use the inverse of the distance between the residential areas as the weight, which is new tothe literature. Third, we analyze the direction and intensity of the income comparison simultaneously. We
analyze a Japanese sample aged 20 or over in seven waves from 2011 to 2017. The results yield several findings.
The sign of the coefficient of comparison income for the overall sample and low-income group is negative inalmost all cases, using equivalent household income as an explanatory variable. Therefore, people may havefeelings of relative deprivation when others earn more income, even if we control for individual fixed effects
without assuming the cardinality of utility and define the reference group using several individual and regional
attributes.

Kenichi Shiraishi
Department of Social Welfare, Gunma University of Health and Welfare
191-1, Kawamagari-machi, Maebashi-shi, Gunma, 371-0823, Japanshiraishi@shoken-gakuen.ac.jp
Kazuto Sumita
Department of Economics, Toyo University
5-28-20, Hakusan, Bunkyo-ku, Tokyo, 112-8606, Japan
sumita@toyo.jp
Kazuki Kamimura
Hirao School of Management, Konan University
8-33, Takamatsu-cho Nishinomiya, Hyogo, 663-8204, Japan
kammrkzk@gmail.com

Shohei Okamoto
Graduate School of Economics, Keio University
2-15-45, Mita, Minato-ku, Tokyo, 108-8345, Japanshohei.z2@keio.jp
Kohei Komamura
Department of Economics, Keio University
2-15-45, Mita, Minato-ku, Tokyo, 108-8345, Japanbzr05433@nifty.ne.jp

Acknowledgement: We thank Dr. Kouichi Kume and Dr. Kayo Nozaki for their helpful comments. Keio
University provided the secondary data on 'the Japan Household Panel Survey and the Keio Household
Panel Survey.'

Relative income and life satisfaction: Who compares theirincome to whose income and to what extent?
Kenichi Shiraishi1, Kazuto Sumita2, Kazuki Kamimura3, Shohei Okamoto4 and Kohei Komamura5
1

Department of Social Welfare, Gunma University of Health and Welfare; 191-1, Kawamagari-machi,

Maebashi-shi, Gunma, 371-0823, Japan2

Department of Economics, Toyo University; 5-28-20, Hakusan, Bunkyo-ku, Tokyo, 112-8606, Japan

3

Hirao School of Management, Konan University; 8-33, Takamatsu-cho Nishinomiya, Hyogo, 663-8204,

Japan
4

Graduate School of Economics, Keio University; 2-15-45, Mita, Minato-ku, Tokyo, 108-8345, Japan

5

Department of Economics, Keio University; 2-15-45, Mita, Minato-ku, Tokyo, 108-8345, Japan

ORCID: Kenichi Shiraishi (0000-0001-6656-9564), Kazuto Sumita (0000-0001-7791-356X), Kazuki
Kamimura (0000-0002-4404-799X), Shohei Okamoto (0000-0002-8580-5291), Kohei Komamura (00000001-9955-5943)

Corresponding author: Kenichi Shiraishi (e-mail: shiraishi@shoken-gakuen.ac.jp, TEL: +81-27-2530294)

Acknowledgment: We thank Dr. Kouichi Kume and Dr. Kayo Nozaki for their helpful comments. Keio
University provided the secondary data on 'the Japan Household Panel Survey and the Keio Household Panel
Survey.'Abstract: Relative income is considered a key to explain the paradox that, since the end of the Second
World War, an increase in per capita income does not raise the average happiness or life satisfaction inwestern countries and Japan. This study uses comparison income as the measure of the relative income andverifies the sign of the coefficient of comparison income in terms of life satisfaction by conducting microeconometric analysis. This study offers three main contributions. The first is to estimate the life satisfaction
equation by using the fixed effects ordered logit model, which previous studies rarely consider. Second, toestimate the average income of the reference group, we use the inverse of the distance between the residential
areas as the weight, which is new to the literature. Third, we analyze the direction and intensity of the income
comparison simultaneously. We analyze a Japanese sample aged 20 or over in seven waves from 2011 to2017. The results yield several findings. The sign of the coefficient of comparison income for the overall
sample and low-income group is negative in almost all cases, using equivalent household income as an
explanatory variable. Therefore, people may have feelings of relative deprivation when others earn moreincome, even if we control for individual fixed effects without assuming the cardinality of utility and define
the reference group using several individual and regional attributes.

1

Keywords: Life satisfaction, Fixed effects ordered logit model, Inverse of distance, Relative income,
Reference group2

1. Introduction
Since the end of the Second World War, an increase in per capita income does notincrease average happiness or life satisfaction in western countries and Japan. Thisphenomenon is also known as the Easterlin paradox (Easterlin 1974; Easterlin 1995).
While this contradicts the assumption in traditional economics that the marginal utility ofincome is positive, the concept of relative income can explain these phenomena. Relative
income, which denotes the income level as compared with one’s past income or that ofothers, affects happiness through adaptation or social comparisons. The effect of income
increase on happiness is temporary and gradually disappears through adaptation. Social
comparisons are related to both the perception of self and others. In this study, we focus on
the relationship between social comparison and life satisfaction. Many studies highlight theimportance of relative income as a determinant of the level of happiness when one’s
income is compared to that of other important people. Even if an individual’s income rises,
the individual’s level of happiness might decrease when people in the surrounding areas
get an income raise comparatively equal to or more than that of the individual.
In economics, policy implications change depending on whether variables ofrelative income are specified in the utility function.1 For example, if an increase in one’s
consumption decreases the happiness of others and, therefore, has negative externality, thedegree of distortion of the progressive income tax and the consumption taxation varies
depending on whether relative income is considered, and structures of optimal taxation

1 Clark, Frijters, and Shields (2008) presented the implications for economic theory and policy design which

consider relative income of social comparisons and adaptation issues in relation to economic growth, labor
supply, wage profiles, optimal taxation and consumption, savings and investment, and migration.

3

may be altered. Furthermore, the poverty line is to be set differently depending on whether
relative income is considered. Thus, it is imperative to understand how relative income
affects individuals’ happiness empirically.
Empirical formulations of relative income in the subjective well-being equation can
be classified into two methods. One method uses the income of the reference group as acomparison income (McBride 2001; Luttmer 2005), while the other uses the income
evaluated relative to the reference group as a relative income (Ferrer-i-Carbonell 2005;
Oshio, Nozaki, and Kobayashi 2011).2 Rather than relative income, comparison income as
an explanatory variable is desirable to avoid multicollinearity between relative income andown income.
However, there is still no consensus on the sign of the coefficient of comparison
income. We can explain this lack of consensus as due to the difference in probablemechanisms. Senik (2004) points out the existence of both the negative effect ofcomparison effects and the positive effect of information effects. Comparison effects are
related to jealousy. Information effects are related to an ambition or signal effect, and theincome of the reference group contains its future prospects. She points out that the role ofinformation depends on the degree of the rapidly changing context where the relative
position is unstable. Kingdon and Knight (2007) point out the possibility of positive andnegative effects concerning the sign of the coefficient of comparison income. Feelings ofrelative deprivation such as envy, aspirations, and shame constitute a negative effect.
However, a positive effect involves (1) altruism or fellow consciousness, (2) share of risk
2 The examples of relative income include the difference between own income and the comparison

income, as well as the normalized rank in terms of the income within the reference group.

4within the community, and (3) proxy variables of social wage (such as the enhancement oflocal public goods). They highlight that people are altruistic towards others in a close
community. Studies such as those by Senik (2004), Senik (2008), and Kingdon and Knight(2007) find positive effects from comparison income. However, many empirical studies
find negative effects of comparison income (Blanchflower and Oswald 2004; Clark and
Oswald 1996; Ferrer-i-Carbonell 2005; Luttmer 2005).
Furthermore, there are various unresolved issues regarding the methods in previous
studies, which includes the estimation methods, the definition of the reference group, andthe direction and intensity of income comparison. First, personality is mentioned as amajor determinant of subjective well-being (Frey and Stutzer 2002). If there is acorrelation between the unobserved personal attributes and explanatory variable, thiscorrelation will result in a coefficient bias. Thus, to control such unobserved heterogeneity,
we should use an estimation method that controls individual fixed effects, though many
empirical examples do not do so (Blanchflower and Oswald 2004; Clark and Oswald 1996;
Kingdon and Knight 2007; McBride 2001; Oshio, Nozaki, and Kobayashi 2011; Oshio and
Urakawa 2012; Mizuochi 2017). Some studies estimate a linear fixed effects model by
implicitly assuming the cardinality of utility (Clark, Westergard-Nielsen, and Kristensen
2009; Luttmer 2005; Senik 2008).3 Prior empirical studies that do not assume cardinality
try to avoid the correlation between the individual random effects and the explanatory
variables by using subjective well-being data of an ordinal scale having three or more

3 Ferrer-i-Carbonell and Frijters (2004) report that if the fixed effects are controlled in the happiness function,

the results do not change substantially between linear and nonlinear estimation. However, it is not verified
with various data sets in various countries.

5

values. 4 Ferrer-i-Carbonell (2005), Senik (2004) and Urakawa and Matsuura (2007)
estimate the ordered probit model which incorporate the Mundlak transformation by
assuming that individual random effects depend on the mean values of explanatory
variables proposed by Mundlak (1978). In practice, however, the relationship between
individual random effects and the time-varying explanatory variables is not necessarily
linear. In such a case, Mundlak type estimators may yield inconsistent and inefficientestimators in nonlinear models like logit, probit, and ordered probit and logit (Goetgeluk
and Vansteelandt 2008; Brumback, Dailey, Brumback, Livingston, and He 2010).
Second, regarding the definition of the reference group, some studies definereference groups in terms of attributes such as individual attributes (McBride 2001; Oshio,
Nozaki, and Kobayashi 2011), regional attributes (Blanchflower and Oswald 2004;
Luttmer 2005; Clark, Westergard-Nielsen, and Kristensen 2009), and occupational
attributes (Clark and Oswald 1996; Senik 2004; Senik 2008). A few prior studies definereference groups in terms of both the residential regional attributes and individual
attributes. However, we define groups by both regional attributes and individual attributes
in detail. Prior studies did not do so to avoid small sample sizes for the reference group(Ferrer-i-Carbonell 2005; Kingdon and Knight 2007). For example, Ferrer-i-Carbonell(2005) divide their sample by education and age group into five categories, but divide theregions into only West and East Germany.
Third, few studies analyze the direction and intensity of income comparison
regarding subjective well-being simultaneously except Clark and Senik (2010), where

4 Brown, Gray, and Roberts (2015) estimate the effect of comparison income with fixed effects ordered logit

model, but they do not describe the details of the estimation method.

6

several limitations exist. Clark and Senik (2010) fail to obtain the income of the referencegroups and compare the income of the individual with that of the reference group. For
example, their analysis shows that people who compare themselves with colleagues havesignificantly higher happiness levels than those who compare themselves with friends andthe general public. However, there is a possibility that the average income of colleagues islower than that of other reference groups. We cannot verify this conjecture as there is no
direct information on the income of different reference groups. Furthermore, since theestimation method is based on the ordinary least squares estimation using the cross-section
data, the fixed effects are not controlled.
This study empirically verifies the sign of the coefficient of comparison income
and clarifies the parties involved in the comparison of one’s income with that of others andthe extent to which the comparison is made. We estimate the life satisfaction equation
using Japanese panel data and carefully verify the validity of the method of identifying
elements such as variables, the scope of the reference groups, and the method of estimation.
In this research, we conduct a micro-econometric analysis of life satisfaction and mainly
analyze the effect of comparison income on it. The main contributions of this study inrelation to previous works are as follows. First, this study performs an estimation with thefixed effects ordered logit model, which few studies employ. Therefore, it is unnecessary
to assume the cardinality of utility, and it is possible to control unobserved time-invariant
heterogeneity such as personality, which is regarded as a major determinant of life
satisfaction. Controlling unobserved heterogeneity is very important to avoid bias in thecoefficient estimates or spurious correlation. Moreover, we deal with the potential
endogeneity of the reference group in part to verify the effects of comparison income on7

life satisfaction. In other words, we perform an additional analysis that limits observations
to non-movers to deal with the endogenous nature of the reference group.
Second, this study defines the reference groups by considering both detailed
residential area and individual attributes. As a regional attribute, the average neighboring
income is calculated based on the inverse of the distance between the residential areas of
the respondents as a weight.5 It is possible to calculate the neighboring income naturally ona nationwide basis by attaching a heavier weight to the observation in the near area.
Moreover, by calculating the average of the neighboring income weighted by the inverseof the distance by conditioning the individual attributes, we can also define the referencegroups reflecting not only the residential area in details but also the individual attributes
such as gender, age, and educational background.
Third, this study analyzes the direction and intensity of income comparison
simultaneously. Therefore, we divide the research work into three main parts to clarify who
compares their income to whose income and to what extent. First, regarding who, weestimate the life satisfaction equation based on the samples divided by individual attributes.
Next, regarding whose income, the average income is calculated by defining the referencegroups according to 11 attributes on spatially weighted individual attributes, individual
attributes, regional attributes, spatially weighted occupational attributes, and spousal
attributes. Regarding to what extent, we compare the estimates of the coefficients ofcomparison income based on a linear fixed effects model, which is easy to compare.

5 The inverse of the distance used as weights is normalized to sum up to 1.

8

The structure of this study is as follows. Section 2 explains the analysis method andthe characteristics of this research by making comparisons with the previous studies.
Section 3 explains the data employed. Section 4 estimates the life satisfaction equation
with the whole sample, as well as the divided samples, and empirically clarifies who
compares their income to whose income and to what extent. Section 5 concludes.

2. Methods
In this research, we conduct a micro-econometric analysis of life satisfaction andmainly analyze the effect of the comparison income on it. There are two primary methods
to calculate the comparison income. The first method is to estimate the wage equation andcalculate the income estimate for each individual, which is the method used by researchers
like Clark and Oswald (1996). The second method is to define the reference group andcalculate the average or median value, which is the method used in studies like Ferrer-iCarbonell (2005). This latter method is further divided into two methods; one that
calculates the estimate from internal data and the other from external data. We use theaverage income of the reference group in calculating the comparison income from theinternal data. In the following section, we describe the analysis methods.

2.1 Empirical model
We estimate a model with the following latent variable as the dependent variable.
𝑦𝑖𝑡∗ = 𝛼1 𝑎𝑖𝑖𝑡 + 𝛼2 𝑐𝑖𝑖𝑡 + 𝑥𝑐𝑖𝑡′ 𝛽 + 𝑐𝑖 + 𝜀𝑖𝑡 , 𝑖 = 1, … , 𝑁, 𝑡 = 1, … , 𝑇.

(1)

Here, 𝑦𝑖𝑡∗ is a latent variable indicating the life satisfaction of individual i at time t. The
observable variable is a discrete variable of the ordered scale, taking a value from 1 to 10
as follows:9

if mj−1 < 𝑦𝑖𝑡∗ ≤ 𝑚𝑗 ,𝑦𝑖𝑡 = 𝑗(j = 1, ⋯ , 10)

where m0 = −∞ and m10 = ∞.6 𝑎𝑖𝑖𝑡 is a variable for own income, and 𝑐𝑖𝑖𝑡 is a variable
for the comparison income. Furthermore, 𝑥𝑐𝑖𝑡 is a vector of control variables, 𝑐𝑖 denotes
the unobserved individual heterogeneity that affects life satisfaction, and 𝜖𝑖𝑡 is a stochastic
error term. We are interested in the coefficient of comparison income, 𝛼2 . We expect that
this coefficient will be negative if the comparison effects are dominant and positive if theeffects related to attributes such as altruism, regional public goods, and information effects
are dominant. In the case of the reference group based on spatially weighted individual
attributes, regional attributes, and spatially weighted occupational attributes, thecomparison income is a weighted average of the income of the respondents living in thevicinity within a d km radius from the respondents. It is calculated as follows:𝑐𝑖 = 𝑤𝑔(𝑑) × 𝑎𝑖,
where 𝑤𝑔(𝑑) is the spatial weight matrix and the (j, k) element of the matrix is the inverseof the distance between the central point7 of municipality j, where the respondent i lives
and that of municipality k where the respondent who belongs to the reference group g lives.
The row elements are normalized such that they sum to 1 by convention. The element of
this spatial matrix is 0 if the distance is over d km or survey year is different. Moreover, 𝑐𝑖
and 𝑎𝑖 are vectors of the comparison income and own income. In the case of the referencegroup based on regional attributes, d is defined as 30 km. In the case of the reference groupbased on spatially weighted individual attributes and spatially weighted occupational

6 Though life satisfaction from the questionnaire can be from 0 to 10, it is not possible to make stableestimations as the proportion of the observations with life satisfaction levels of 0 and 1 is small. Thus, thesevalues are integrated into one value.
7 The central point of the municipality is defined as the location of the city hall.

10

attributes, d is defined as infinity. Since a heavier weight is attached to the observation in
the near area, we believe the reference groups based on spatially weighted individual
attributes and spatially weighted occupational attributes also reflect regional attributes. We
will describe the reference groups of respondents in detail later. The estimated parameters
will be biased if the unobserved personal attributes affect life satisfaction and the
explanatory variables; thus, we adopt the fixed effects ordered logit model to control
unobserved time-invariant heterogeneities such as personality factors.

2.1.1 Estimation method of fixed effects ordered logit model by the MDE
model
The fixed effects ordered logit model is estimated using the method of Das and van
Soest (1999) (hereinafter referred to as the Minimum distance estimation or MDE model)
and the method proposed by Mukherjee, Ahn, Liu, Rathouz, and Sanchez (2008)(hereinafter referred to as the Blow-Up and Cluster or BUC model). In the MDE model,
we perform the following two-step estimation. In the first step, by combining the adjacentcategories of yit , taking values from 1 to 10, it is possible to compute nine pairs of thebinary variables, 𝑆𝑗,𝑖𝑡 (j = 2, 3 , ... 10), and estimate the fixed effects logit model of
Chamberlain (1980) for each.
∗
′
𝑆𝑗,𝑖𝑡
= 𝑥𝑖𝑡
𝜃𝑗 + 𝑐𝑖 + 𝜀𝑗,𝑖𝑡 , 𝑖 = 1, … , 𝑁, 𝑡 = 1, … , 𝑇,(2)where 𝑥𝑖𝑡 is a vector of explanatory variables including own income and comparison
income. In this model, we assume that 𝜖𝑗,𝑖𝑡 independently follows the logistic distribution
and estimate the following conditional logit model.

11𝑗
𝑃𝑖 (𝑆𝑗,𝑖1 , … , 𝑆𝑗,𝑖𝑇 |𝑥𝑖1 , … , 𝑥𝑖𝑇 , 𝑐𝑖 , 𝑠𝑗,𝑖 ) =′∏𝑇𝑡=1 exp(𝑆𝑗,𝑖𝑡 𝑥𝑖𝑡𝜃𝑗 )′∑𝑑∈𝐷𝑗,𝑖 ∏𝑇𝑡=1 exp(𝑑𝑗,𝑖𝑡 𝑥𝑖𝑡
𝜃𝑗 )

.

(3)

Here, 𝑠𝑗,𝑖 indicates the sum of 𝑆𝑗,𝑖𝑡 that the i-th individual can take in the T periods,
and we analyze 𝑆𝑗,𝑖 = (𝑆𝑗,𝑖1 , … , 𝑆𝑗,𝑖𝑇 ) on the condition that the sum of T binary outcomes is𝑠𝑗,𝑖 . Furthermore, defining the set 𝐷𝑗,𝑖 = {𝑑𝑗,𝑖 |𝑠𝑗,𝑖 = ∑𝑇𝑡=1 𝑑𝑗,𝑖𝑡 } to have all possiblecombinations of 𝑠𝑗,𝑖 with a value of one and 𝑇 − 𝑠𝑗,𝑖 with a value of zero, we obtain aconditional likelihood function. However, due to this formulation, individuals who do nottake 0 or 1 at all during the observation period are excluded from data analysis. We
estimate this model for nine pairs of binary variables and find nine pairs of 𝜃̂𝑗 . In thesecond stage, we can obtain β and its variance-covariance matrix using the minimum
distance estimator for the common elements of the estimated parameters as follows. The
estimates obtained in the first step are stacked in the column direction, and 𝜃̂ =′(𝜃̂2′ , 𝜃̂3′ , ⋯ , 𝜃̂10)′ is obtained. This result is a 9 ∙ K × 1 column vector, where K is thenumber of regressors. Given the common element 𝛽 in 𝜃̂𝑗 , in MDE, we will estimate 𝛽 byminimizing′−1
D(𝛽) = (𝜃̂ − 𝐻𝛽) 𝑉[𝜃̂] (𝜃̂ − 𝐻𝛽).

Here, H is a matrix of nine stacked K-dimensional identity matrices, and V[𝜃̂] is thevariance-covariance matrix of the stacked estimation in the first stage. We can obtain the
MDE of 𝛽 and its variance-covariance matrix as follows:−1𝛽̂ = (𝐻 ′ 𝑉[𝜃̂] 𝐻)−1−1
𝐻 ′ 𝑉[𝜃̂] 𝜃̂,−1−1V[𝛽̂ ] = (𝐻 ′ 𝑉[𝜃̂] 𝐻) .

12

Here, we use 𝛽̂ as an estimate of the fixed effects ordered logit model by the MDE model.

2.1.2 Estimation method by the BUC model
The BUC model is a method proposed by Mukherjee, Ahn, Liu, Rathouz, andSanchez (2008). As with the estimation method of the MDE model, by grouping theadjacent categories, nine pairs of estimates maximize the likelihood function imposed by
the constraints in which the estimated values of the respective coefficients to explain thevariables 𝑆𝑗,𝑖𝑡 (j = 2, 3, ... 10) are the same.
10
BUC (𝜃)L𝑁𝑗= ∑ ∑ 𝑙𝑛{𝑃𝑖 (𝑆𝑗,𝑖1 , … , 𝑆𝑗,𝑖𝑇 |𝑥𝑖1 , … , 𝑥𝑖𝑇 , 𝑐𝑖 , 𝑠𝑗,𝑖 )}.

(4)

𝑗=2 𝑖=1𝑗Here, 𝑃𝑖 is the conditional logit model (3). In this estimation, the restriction that 𝜃̂2 = ⋯ =𝜃̂10 = 𝛽̂ is imposed, where 𝛽̂ is the estimator of the fixed effects ordered logit model by
the BUC model.

2.2 Defining the reference groups
We define the reference groups using spatially weighted individual attributes,
individual attributes, regional attributes, spatially weighted occupational attributes, andspousal attributes. The average income of the reference group is calculated individually,
assuming that the members of the reference group with whom comparison is made vary foreach. Previous studies often performed analysis, assuming that the average income of thereference group is the same within the members of the same reference group. However, in
reality, such a group should be different for individuals, being more affected by neighbors
than those living at a distance. For example, even in the same prefecture, the residents in13

cities or towns near Tokyo are affected by the residents in Tokyo’s metropolitan area while
the residents in suburban cities or towns are likely affected by the residents in local
prefectures. Therefore, we calculate the average income of the neighbors by using theinverse of the distance from the municipality where other households reside and use it as
the comparison income.

2.3 Explanatory variables
Next, we elaborate on the explanatory variables of life satisfaction. Frey andStutzer (2002) suggest the following five factors are determinants of happiness: 1)
Personality factors, 2) Socio-demographic factors, 3) Economic factors, 4) Contextual andsituational factors, and 5) Institutional factors.
Personality factors are particularly influenced by two factors, which aretemperament predisposition and traits and cognitive dispositions. In this study, since we
conduct the analysis by controlling the individual fixed effect, we can control personality
when it does not change during the follow-up.
Age, gender, marital status, and educational background constitute the sociodemographic factors. Several studies report that happiness has a U-shaped relationshipwith age, and we included the squared terms of age, as well as age in logarithmic form. We
also included marital status in our model.
Economic factors include income, unemployment, and inflation rate. We assume
that not only own income, but also comparison income, affect happiness. We define
comparison income as the income of a closely related group. We use unemployment as a14

dummy variable because unemployment affects life satisfaction significantly, even thoughother variables are controlled.
Contextual and situational factors refer to human relations, health, and employment
conditions. We add the average volunteer participation rate of the neighbors as proxyvariables of social capital and add health and work status as explanatory variables.
Institutional factors include the political system and governance to the government.
In this study, we control the effects of the institutional factors indirectly by including year
dummies as explanatory variables since it reflects changes in the institutional factors.

3. Data
3.1 Japan household panel survey
We use the Japan Household Panel Survey (JHPS/KHPS) to estimate the model.
This survey is a combination of the former Japan Household Panel Survey (JHPS) and the
Keio Household Panel Survey (KHPS), which were previously conducted and managed as
separate surveys. The characteristics of the surveys, such as the data structure and sample,
are as follows.8
The KHPS began in 2004, surveying 4,005 households, and the JHPS began in2009 surveying 4,000 households. Approximately 1,400 and 1,000 new panel members of
the KHPS are recruited in 2007 and 2012 respectively. In both surveys, households are
selected through a stratified two-stage sampling method throughout Japan. The survey

8 For precise information, see Panel Data Research Center at Keio University

https://www.pdrc.keio.ac.jp/en/paneldata/datasets/jhpskhps/ (accessed on August 20, 2018)15

subjects of the KHPS are selected from men and women aged 20 to 69 nationwide, andthose of the JHPS are selected from men and women aged 20 or above nationwide.
Although the sampling populations overlap, ultimately, there is no overlap of KHPS and
JHPS respondents. The two data sets have been combined since 2015 as the JHPS/KHPS
since they contain questions that are either the same or similar.
Poverty rate after taxes and transfers in Japan in 2015 is 15.7% according to theOrganization for Economic Co-operation and Development (OECD) Income Distribution
database, which is very high among the OECD countries. The poverty rate is calculated as
the ratio of the number of people whose equivalent disposable income falls below thepoverty line, which is half of the median equivalent disposable income of the total
population. We will examine whether the effect of comparison income work on life
satisfaction in countries with high poverty rates. We use seven waves of JHPS/KHPS run
annually from 2011 to 2017; they contain a questionnaire about life satisfaction since 2011.
Explanatory variables are created from the data of JHPS/KHPS. In the following sections,
we describe how to prepare the variables of life satisfaction, income, social capital, andhealth condition. Table A1 and A2 in the appendix summarize descriptive statistics ofvariables used for the analysis.

3.2 Life satisfaction
In JHPS/KHPS, respondents are asked to rate satisfaction with their general life in
11 levels on a scale of 0 to 10: 0, not at all satisfied; 5, neither satisfied nor dissatisfied;and 10, fully satisfied. General life satisfaction is a validated scale to measure subjective

16

well-being, considering various aspects of satisfaction with life (such as finance, job, andhealth) (Praag, Frijters, and Ferrer-i-Carbonell 2003).

3.3 Income
Equivalent household income is calculated by dividing the total annual after-tax
household income by the square root of the number of household members. We use therespondents’ total annual before-tax income as individual income.9 We obtain real incomeby creating a price index that reflects regional and intertemporal differences from theconsumer price regional difference index (by prefecture) and the general index that
excludes the imputed rent of owned house from the time series consumer price index(Japan, 2015=100). To calculate the average income of neighbors, we measure thedistances between respondents and calculate the weighted average of the income of people
from surrounding areas with the weights of the inverse of the distance. We use income
surveyed in JHPS/KHPS in calculating the income of people from surrounding areas. Since
JHPS/KHPS surveys the information about the city where individuals reside, using the
CSV address matching service provided by the University of Tokyo Spatial Information
Science Research Center, we obtained the latitude and longitude of the location of the city
hall of the individual's place of residence, then we measured the distances between
respondents.10

9 In JHPS/KHPS, respondent’s total annual after-tax income is not surveyed.
10 According to Miura (2015), the distance between the residential areas is conveniently measured in thefollowing manner with the latitude as φ, the longitude as λ, and the number of the subscript as the point. L=
6370 arccos(sin φ1 sin φ2+ cos φ1 cos φ2 cos(λ1 − λ2))17

We used gender, age range, full-time employment dummy, and a dummy foruniversity graduates as attributes of individuals to be conditioned and to calculate theaverage income in the case of the reference groups of spatially weighted individual
attributes, as well as individual attributes. The age range of the reference group is defined
as five years younger and older than the individual concerned, as McBride (2001) proposes.
For example, if an individual is 45 years old, the age range of the reference group is 40 to
50 years old.
We use the logarithmic value of the income of the reference group for comparison
income. We expect a negative sign of the coefficient of comparison income if comparison
effects occur and a positive sign if information effects or altruism occur. The explanatory
variables for the analysis include: age and squared age of the respondent, own income,comparison income, participation rate of neighbors in volunteer work, spouse dummy,
employment state (regular employee dummy, non-permanent employee dummy, selfemployed person dummy, and unemployment dummy), homeowner dummy, health(psychosomatic symptom score), and year dummies. Time-invariant variables such as
gender and educational background are not included in explanatory variables because thefixed effects are controlled.
The reference groups for income comparison in this study are based on spatiallyweighted individual attributes, individual attributes, regional attributes, spatially weighted
occupational attributes, and spousal attributes. 11 Furthermore, four types of spatially

11 When using spouse as the reference group, we restrict the sample to married respondents.

18

weighted individual attributes, as well as individual attributes, are defined as the referencegroups from the attributes of the respondents as follows:(G1) (i) Age, (ii) Gender, and (iii) Marital status(G2) (i) Age, (ii) Gender, (iii) Educational background, and (iv) Marital status(G3) (i) Age, (ii) Gender, (iii) Occupational form, and (iv) Marital status(G4) (i) Age, (ii) Gender, and (iii) Educational background
For the four types of spatially weighted individual attributes, we calculate thecomparison income from the weighted average of the income of the neighbors with thesame attributes for each of the four types using the inverse of the distance as the weight.
For example, the reference group in (G1) consists of respondents who are in the age rangeof five years younger and older than the individual concerned and are of the same genderand have the same marital status using the inverse of the distance as the weight. Therefore,
we measure the comparison income within the JHPS/KHPS data set without extrapolatingfrom external data. Similarly, regarding reference groups (G2) to (G4), we calculate theaverage incomes of neighbors with the same attributes using the inverse of the distances as
the weight.
We create a comparison income based on the regional attributes; that is,
respondents within a 30 km radius for each respondent using the inverse of the distances as
weight. The advantages and disadvantages of limiting the scope of the region as areference group are as follows. The reference group can reflect regional attributes morestrongly. However, the reference group will have fewer observations. The sample size
becomes small because the reference group will have many conditions to consider in the19case of spatially weighted individual attributes and spatially weighted occupational
attributes. Thus, the disadvantage of limiting the range of the area emerges strongly, andthe spatial weight is created without specifying the range in those cases. Therefore, since
the number of conditions to consider is small for reference groups based on regional
attributes, we set 30 km as the distance to reflect strong regional attributes. Depending on
the type of reference group, there is no uniformity in the scope of the region because we
take the trade-off relation between the advantages and disadvantages into consideration.
Table 1 shows the number of respondents within a 30 km radius from the 10th
percentile to the 99th percentile for all observations from 2011 to 2017. We calculate theresult as follows:𝑧 = 𝑤𝑖30 × 𝐽,
where 𝑤𝑖30 is the spatial weight matrix. The (i, k) element of the matrix takes one if the
respondent i lives within a 30 km radius from the municipality where respondent k lives inthe same year; otherwise, it is 0.12 𝐽 is a vector where all elements are 1. By sorting thenumber of the respondents living within a 30 km radius in ascending order, the number atthe 10th percentile is 4, and 16 is at the 25th percentile point, in that order. The comparison
income is treated as the missing value if no one lives in the above distance. Since themedian of the respondents living within the 30 km radius is 53, the members of thereference group will be very small when we define a reference group by combining
multiple attributes (individual, regional, and occupational attributes). For example, for(G1), a reference group based on spatially weighted individual attributes, we have to select12 The rows of the spatial weight matrix are not standardized such that they sum to one.

20

its members from the respondents within a 30 km radius with a median of 53 who are ofsimilar age (age range of five years younger and older), the same gender, and the same
marital status. That is, if we restrict the geographical scope within a 30 km radius. Thus,
there may be no members in the reference group for some respondents. Therefore, we do
not restrict the geographical scope for spatially weighted individual attributes and spatiallyweighted occupational attributes. However, we restrict the geographical scope within a 30
km radius in the reference group, which we define using only the regional attribute because
many respondents have a certain number of members of the reference group. If the
distance is much shorter than 30 km, the problem of small sample sizes of the referencegroup becomes severe, and if the distance is much longer than 30 km, the reflection of theregional attribute on the reference group may be weak. Thus, we define the area within a
30 km radius for the reference group of regional attributes.
Table 1: The number of respondents living within 30 km radius
The percentile point

The percentile values of z

10%

4

25%

16

50%

53

75%

206

99%

835

Note: z is calculated as follows: 𝑧 = 𝑤𝑖30 × 𝐽, where 𝑤𝑖30 is the spatial weight matrix. The element of
the matrix takes one if the distance between the respondents is within 30 km and the survey year issame; Otherwise, it takes 0. 𝐽 is a vector where all elements are 1.

We create one type of comparison income based on spatially weighted occupational
attributes by calculating the weighted average of the income of people from surrounding
areas using the inverse of the distance as a weight. The following attributes define the21comparison subjects: (i) Age, (ii) Gender, (iii) Form of employment, (iv) Company size,and (iv) Nature of the work.

3.4 Social capital
Putnam (1995) defined social capital as “the characteristics of society such as
network, norms, and trust that enhance social efficiency by encouraging people’s
cooperative activities.” Previous studies report that social capital exerts a positive
influence on an individual’s health and well-being (Matsushima and Matsunaga 2015;
Murayama, Fujiwara, and Kawachi 2012). We can classify the methods to measure social
capital into two dimensions: the individual level and the group level. As the individuallevel of social capital may be endogenous in relation to life satisfaction, we adopt thegroup-level social capital as an explanatory variable.
Specifically, we calculate the weighted average of the participation rate involunteer activities amongst the people in the surrounding areas (1 if they participate
almost every day or several times per week; 0, otherwise) with the weight of the inverse of
the distance normalized such that it sums to 1. This calculation excludes individuals who
reside at a distance of more than 30 km away from the respondent. Participation involunteer activities is often used as a proxy variable of reciprocity, which is one
component of social capital (Matsushima and Matsunaga 2015; Saxton and Benson 2005).
In line with these existing findings, the greater the participation rate, the higher thealtruism and reciprocity. We expect that social capital has a positive influence on life
satisfaction.

22

3.5 Health condition
As a proxy variable for health, we can use the following three kinds of variables:(1) Self-rated health (SRH), (2) Objective health condition, and (3) Psychosomatic
symptom score.
For SRH, the following question is asked in the survey: How is your healthnormally? The respondent chooses one of the following: 1) Good, 2) Pretty good, 3)
Normal, 4) Not so good, and 5) Bad.
For an objective health condition, the following questions are asked: Did you
receive medical treatment, or were you hospitalized last year? and What types of problems
were noted in the examination results?
For the psychosomatic symptom score, questions pertaining to the symptoms are as
follows: 1) Headache or dizziness, 2) Palpitations (or out of breath) 3) Digestive problems,4) Back, lower back, and shoulder pain, 5) Tire easily, 6) Catch cold easily, 7) Often
become irritated, 8) Trouble falling to sleep, 9) Find seeing people tiresome, 10) Lost work
concentration, 11) Dissatisfied with life, and 12) Anxiety about the future. For eachquestion, respondents select one of the following choices: 1) Never, 2) Rarely, 3)Sometimes, and 4) Often. The scores for these answers range from 0 for Never, 1 for
Rarely, 2 for Sometimes, and 3 for Often. The scores for the 12 answers are summed up
and are used as the psychosomatic symptom score.
Although SRH can be comprehensive enough to include overall health (Chandola
and Jenkinson 2000), there is a risk of generating bias that would leave to a largecoefficient. The risk arises because the explanatory variable of life satisfaction is also

23

subjective, and reverse causality and confounders (such as mood at that time) might beproblematic. However, the objective health condition might capture only a portion of thehealth, and the bias due to measurement error may underestimate the coefficient of health.
Since the psychosomatic symptom score requires the symptoms, we expect that thatmood and the environment at that time will have little effect. Thus, it may be more
appropriate as the objective measurement of health than SRH and as the morecomprehensive scale than the objective health condition variable. Thus, we use thepsychosomatic symptom score as the variable for health conditions. However, caution isrequired as the scale focuses only on the limited negative aspects of the health condition,and it is not a complete proxy variable for the health condition.13

4. Results
In the following section, we estimate the life satisfaction equation with the wholesample, as well as the divided samples, by using the comparison income of the various
reference groups mentioned above.

4.1 Analysis of the effect of comparison income
Table 2 summarizes the results of the estimated effects of the comparison income
based on various reference groups which satisfy the significance level by using the wholesample and divided samples by gender and income group. Based on the seven-year average

13 Since we are not able to find appropriate instrumental variables, we do not perform an instrumental

variables estimation in this study.

24income, we classify the sample into a high-income group with above-average income and a
low-income group with below-average income. The coefficients of the comparison income
based on equivalent household income in the whole sample, females, and the low-income
group tend to show comparison effects stably. However, the coefficients of the comparison
income based on individual income show comparison effects in a few cases. We findcomparison effects mainly in women when using equivalent household income as an
explanatory variable because many women depend largely on their husband’s income interms of living standards. The average total income of wives is about three-tenths of theaverage total income of husbands within married respondents of JHPS/KHPS.
By comparing coefficients of comparison incomes of various reference groups, thecoefficients of the comparison income calculated from the equivalent household income of
the individual attributes, (G1) and (G2), and the spatially weighted individual attributes(G4) show stable comparison effects; the direction of income comparison is different
depending on whether or not to consider regional attributes as the reference group.
However, if we divide the sample by gender and income group, comparison effects
are not observed for men except some reference groups. Moreover, comparison effects areobserved in most cases for low-income groups using equivalent household income as an
explanatory variable. However, comparison effects are not observed for the high-income
group except for some reference groups. This observation implies that income comparison
is not symmetric. That is, the increase of the average income of the reference groupdecreases the life satisfaction of the low-income group, but it does not affect the life
satisfaction of the high-income group except for some reference groups. This result is
consistent with the model advocated by Duesenberry (1949).

25

Table 2: The effects of comparison income, JHPS/KHPS 2011–2017
Spatially weighted individual
attributes
(G1)
(G2)
(G3)
(G4)
Whole
sampleE.H.I.a
I.I.b

Male

E.H.I.
I.I.

FemaleE.H.I.
I.I.

26
High
IncomeGroup

E.H.I.

Low
IncomeGroup

E.H.I.

I.I.

I.I.

MDE
BUC
MDE
BUC
MDE
BUC
MDE
BUC
MDE
BUC
MDE
BUC
MDE
BUC
MDE
BUC
MDE
BUC
MDE
BUC

**

***
**

***
*

***
**

Individual attributes
(G1)

(G2)

(G3)

***
**

***
**

***
**

Regional
attributes(G4)

Spatially
weighted
occupational
attributes

Spouse

###
##
###
##

*
**
*
**
***
*

***
*

**

**

**
**

**

***
***

***
***

***
**
***
***
**

***
**
***
**
**

***

**

###
###
###
##

*

*

*
***
***

***
***

###
##
***
**

***
**

***
***

**

##
###
###

Note: *Negatively significant at the 0.10 level; **at the 0.05 level; ***at the 0.01 level. #Positively significant at the 0.10 level; ##at the 0.05 level; ###atthe 0.01 level. There are four types of spatially weighted individual attributes and individual attributes as follows: (G1) (i) Age, (ii) Gender, and (iii)
Marital status, (G2) (i) Age, (ii) Gender, (iii) Educational background, and (iv) Marital status, (G3) (i) Age, (ii) Gender, (iii) Occupational form, and (iv)
Marital status, (G4) (i) Age, (ii) Gender, and (iii) Educational background.
a

E.H.I. means the case of equivalent household income.

b

I.I. means the case of individual income.

For the reference group based on the regional attributes, we expect a negative
coefficient if the comparison effects are dominant and a positive one if the effects of
attributes such as altruism and regional public goods are dominant. Table 2 shows thenegative effects in a few cases. Clark et al. (2009) and Mizuochi (2017) report a
statistically significant positive effect, contrary to the findings of this study. Mizuochi(2017) restrict the sample to some areas in Japan, and thus, we might consider this sample
as a special group. Further, since he does not control the fixed effects, the estimatedcoefficient may be biased. However, in this study, we set the area within a 30 km radius as
the reference group. It is possible that the positive effects that relate to altruism occur only
in narrow areas like residents’ associations or an elementary school districts where there
are many opportunities for daily interaction, which is an important area for future study.
For the reference group based on the spatially weighted occupational attributes, we
see positive effects in the high-income group when using individual income as an
explanatory variable. We could interpret these positive effects as information effects.
However, we observe no positive effects when using equivalent household income as an
explanatory variable. One reason for this result for the high-income group is that theindividual income of the reference group may contain their future prospects. However, theequivalent household income of the reference group may not contain their future prospects.
For example, we imagine a hypothetical case of two husbands with similar occupational
characteristics, as follows:
A: Yearly income the husband earns is 5,000,000 yen; the wife earns 200,000 yen.
B: Yearly income the husband earns is 6,000,000 yen; the wife earns 7,000,000 yen.

27

The occupational attributes of the husband in the case of A are similar to those in
the case of B. However, the occupational attributes of the wife in the case of A are notsimilar to those in the case of B. Thus, it seems natural to conclude that the householdincome in the case of B, which is more than twice as high as that in the case of A, does notcontain future prospects for the husband in the case of A. This example can explain one
reason why we do not see information effects when using equivalent household income as
an explanatory variable. Even in a society like Japan, where relative position is stable, wefind information effects for high-income group.
The analysis of spouses as the reference group shows positive effects opposite to
the comparison effects in both the equivalent household income and the individual income.
Although further investigation will be needed, we believe that it is reasonable to conclude
that the income of a spouse that the respondent shares may affect life satisfaction
positively.
Next, the respondents who moved to a different city are removed to avoid theendogeneity of the reference group. The statistical significance for the coefficient ofcomparison income remains unchanged, and the difference in the estimation result is notlarge. Due to lack of space, we omit this analysis results, but these are available to readers
on request. Table 3 presents the average income level of individuals and various referencegroups before and after moving. In this table, we extract the observations of the same
respondent for two consecutive years whose residential city is different. By averaging
incomes in the former year of these observations, we can calculate the average income
before moving. Moreover, by averaging incomes in the latter year of these observations,
we can calculate the average income after moving. In the case of the equivalent household

28

income of the moving respondents, while the incomes of the household and the referencegroup both tend to decline somewhat after moving, the decline for the reference group isslightly sharper.

Table 3: Average income level of individuals and various reference groups before andafter moving, JHPS/KHPS 2011–2017
Equivalent Household
IncomeAverage
Average
IncomeIncomeBeforeAfter
MovingMoving264.9993
262.1226

Individual Income
Average
Income Before
MovingAverage
Income After
MovingOwn income
310.1248
312.3992
Spatially weighted individual
264.4868 * * 260.5818
315.9993
316.1678
attributes (G1)
Spatially weighted individual
265.2829 * * 261.2426
316.2142
316.7196
attributes (G2)
Spatially weighted individual
265.7696 * 261.939
317.9848
317.7153
attributes (G3)
Spatially weighted individual
265.3324 * * 260.8865
317.3764
317.4481
attributes (G4)
Regional attributes
264.9667 * * 261.8016
314.2572
314.1678
Spatially weighted occupational
265.4507 * 261.7066
316.4102317.0178
attributes
Note: The unit of currency is million yen. Asterisks indicate significant differences (Student's two-sided
t-test, ** p<0.01, * p<0.05). The column of Average Income Before Moving is the average income in
the former year of the observations of the same respondent for two consecutive years whose residential
city is different. The column of Average Income After Moving is the average income in the latter yearof the observations of the same respondent for two consecutive years whose residential city is different.
There are four types of spatially weighted individual attributes as follows: (G1) (i) Age, (ii) Gender, and(iii) Marital status, (G2) (i) Age, (ii) Gender, (iii) Educational background, and (iv) Marital status, (G3)(i) Age, (ii) Gender, (iii) Occupational form, and (iv) Marital status, (G4) (i) Age, (ii) Gender, and (iii)
Educational background.

In Table 4, we discuss the result of analysis of equivalent household income andthe reference groups with individual attributes, (G1) and (G2), and spatially weighted

29

individual attributes (G4). This result is the estimation results of the MDE model and the
BUC model. This table shows comparison effects stably based on whole sample. The
coefficient of comparison income is negative and statistically significant. The participation
rate for volunteers, which is a proxy variable of social capital, is not significant. Lifesatisfaction is high for those earning high-income and living in self-owned housing andlow for those unemployed, resulting in a high score for psychosomatic symptoms.

30

Table 4: Results in the case of equivalent household income and various reference groups in whole samplea, JHPS/KHPS 2011–201731

ln(Age)ln(Age)2ln(Ownincome)ln(Comparison
income)Volunteer
participationrate
Spouse dummy
Regularemployeedummy
Non-permanent
employee
Dummy
Self-employedperson dummy
Homeowner
dummy
Psychosomatic
symptom score
Unemployment
dummy

Individual attributes (G1)b
MDE
BUC
Coeff.
S.E.
Coeff.
-41.837***
11.700-217.04**7.870 ***
2.114
100.65**

S.E.
90.88441.045

Individual attributes (G2)cMDE
BUC
Coeff.
S.E.
Coeff.
-38.658***
11.543-198.90**
7.243 ***
2.080
92.021**

S.E.
86.49638.927

Spatially weighted individual attributes (G4)dMDE
BUC
Coeff.
S.E.
Coeff.
S.E.
-37.301***
11.469 -44.941*
24.882
6.966 ***
2.062
8.355 *
4.472

0.170 ***

0.023

0.173 ***

0.037

0.169 ***

0.023

0.172 ***

0.037

0.169 ***

0.023

0.172 ***

0.037

-0.641 ***0.192-0.705 **0.343-0.452 ***0.132-0.488 **0.213-0.216 ***0.060-0.232 **0.096-0.0340.206-0.0530.398-0.0410.207-0.0600.397-0.0330.208-0.052

0.398

0.004

0.084

-0.0070.174-0.0060.084-0.0200.173-0.0710.084-0.0900.177

0.112*

0.062

0.118

0.114

0.111*

0.062

0.116

0.113

0.111*

0.062

0.116

0.113

0.127 **

0.054

0.112

0.093

0.124*

0.054

0.109

0.093

0.128 **

0.054

0.115

0.093

0.032

0.063

0.038

0.105

0.031

0.063

0.038

0.105

0.025

0.064

0.033

0.106

0.326 ***

0.071

0.341 **

0.145

0.320 ***

0.071

0.335 **

0.144

0.311 ***

0.071

0.328 **

0.146

-0.084 ***0.002-0.084 ***0.004-0.084 ****

0.002

-0.084 ***0.004-0.084 ***0.002-0.084 ***0.004-0.559 ***0.089-0.567 ***0.156-0.559 ***0.089-0.567 ***0.156-0.532 ***0.090-0.539 ***0.157

Note: Significant level: *** p<0.01, ** p<0.05, * p<0.1. Time-dummies are present in all estimates but are not shown.
a

The number of observations and the number of individuals varies depending on the cut-off point which dichotomizes the ordered variable of life
satisfaction.
b

Individual attributes (G1) as the reference group are composed of respondents who are in the age range of five years younger and older than theindividual concerned, are of the same gender, and have the same marital status.

cIndividual attributes (G2) as the reference group are composed of respondents who are in the age range of five years younger and older than theindividual concerned, are of the same gender, and have the same educational background and marital status.
dSpatially weighted individual attributes (G4) as the reference group are composed of respondents who are in the age range of five years younger andolder than the individual concerned, are of the same gender, and have the same educational background using the inverse of the distance as weight.

32

4.2 Direction and intensity of comparison effects
We proceed to analyze the direction and intensity of the comparison effects. For the
nonlinear estimation, we cannot simply compare the magnitude of the marginal effect fromthe estimated coefficients; thus, we compare the estimated coefficients of the comparison
income in the linear fixed effects models. That is, we use the linear model to analyze who
compares their own income to others, who these others are, and to what extent. The
subjects are divided by gender, employment type (regular and irregular), educational
background (university graduate and non-university graduate), marital status (married andunmarried) and income group (high and low). Table 5 summarizes the negative coefficientof the comparison income, which is statistically significant among the 11 reference groups
mentioned above. For equivalent household income, the coefficients of comparison income
are statistically significant at the five percent level in 21 cases. Moreover, regular
employees and the low-income group tend to care about the average income of thereference groups defined by individual attributes. For individual income, the coefficients ofcomparison income are statistically significant at the five percent level in three cases, andwomen particularly care about the average income of the reference groups defined byindividual attributes.

33

Table 5: Comparison of the effects of comparison income, JHPS/KHPS 2011–2017
Equivalent household income
Subject
Reference group
1 Regular employee Individual (G1)
2 Regular employee Individual (G4)
3 Low-income
group
4 Low-income
group
5 Woman
6 Regular employee
7 Non-university
graduate
8 Woman
9 Unmarried person

Coeff.
-0.909-0.765

Individual (G1)

-0.765

Individual (G3)-0.722

Individual (G1)
Individual (G3)
Individual (G3)

-0.673-0.629-0.431

Individual (G2)
Spatially weighted
individual (G4)
Individual (G2)-0.429-0.373

Individual income
Subject
Reference group
1 WomanIndividual (G1)
2 Unmarried
Regional
person3 WomanIndividual (G2)

Coeff.
-0.846-0.451-0.44810 Low-income-0.366group
11 Regular
Spatially weighted -0.314
employeeindividual (G2)12 University
Spatially weighted -0.277
graduate
individual (G4)13 Low-income
Spatially weighted -0.273
groupindividual (G3)14 Unmarried
Spatially weighted -0.263
personindividual (G1)
15 Low-income
Spatially weighted -0.261
groupindividual (G1)
16 Low-income
Spatially weighted -0.249
groupindividual (G4)17 Unmarried
Spatially weighted -0.236
personindividual (G3)18 Regular
Spatially weighted -0.233
employeeindividual (G4)19 Low-income
Spatially weighted -0.225
group
individual (G2)20 Regular
Spatially weighted -0.212
employeeindividual (G3)21 Non-university
Spatially weighted -0.188
graduate
individual (G3)
Note: This is a summary of the negative coefficient of the comparison income, which is statisticallysignificant at least at the five percent level among the 11 reference groups.

34

5. Conclusion
This study empirically verifies the sign of the coefficient of comparison income
and investigate who compares their income to whose income and to what extent byconducting a micro-econometric analysis of life satisfaction. We control individual fixed
effects without arbitrarily assuming the cardinality of utility by using the panel data fromJapan, which reflect the population composition of society over the age of 20 considerably.
The results of our analysis are as follows. Concerning the sign of the coefficient ofcomparison income, whenever the coefficient is significant in whole sample, it is negativein almost all cases except when the spouse is the reference group. Therefore, comparison
effects may be stronger. Meanwhile, in almost all cases, there are few positive effects,
which are related to attributes such as information effects, altruism, and the enhancementof regional public goods. Comparison effects are observed in most cases for the lowincome group, but they are not observed for the high-income group except some referencegroups using equivalent household income as an explanatory variable. This observation
implies that income comparison is not symmetric as with the model advocated byDuesenberry (1949). Therefore, people may have feelings of relative deprivation whenothers earn more income. However, there is no feeling of relative satisfaction when others
earn less income in most cases, even if individual fixed effects are controlled for without
assuming cardinality of utility and detailed individual and regional attributes are
considered in defining reference groups. Only for the high-income group when using
individual income as an explanatory variable do we see positive effects related to
information effects.

35This study has several limitations that provide opportunities for further research.
First, it is necessary to measure the representative value of income by narrowing the areaof the reference group and the range of occupational attributes. In that case, it would benecessary to select observations by random sampling from all over the country to eliminate
local bias; the analysis with the panel data is desirable. For regional attributes as thereference group, it would be ideal to narrow the geographical scope to the level of dailyinteraction, such as residents’ associations and elementary school districts. For
occupational attributes as the reference group, it would be ideal to match the data of
individuals to their workplace and to measure the average income of colleagues of thesame company.
Second, this study uses the weighted average of the participation rate of people involunteer activities from surrounding areas as a proxy variable for social capital. For future
research, it is necessary to explore the possibility of using more comprehensive indicators
which reflect reliability and various elements contained in the social capital as used in Kim,
Subramanian, Gortmaker, and Kawachi (2006).
In conclusion, comparison effects which relate feelings of relative deprivation, suchas jealousy, envy, and shame, are observed to be significant for life satisfaction even if
unobserved time-invariant heterogeneities and various reference groups are considered.
Particularly, people may have feelings of relative deprivation when others earn moreincome because the low-income group tends to care about the average income of thereference group.

36Compliance with Ethical Standards
Ethical approval was not required because this study was based on secondary analysis of
publicly available data.

Conflict of Interest
The authors declare that they have no conflict of interest.

37

Appendix
Table A1: Distribution of Life Satisfaction
Life satisfaction
Frequency

Percentage

0 (low)

486

1.68

1

376

1.3

2

786

2.71

3

2,059

7.11

4

2,401

8.29

5

7,955

27.47

6

3,244

11.2

7

4,341

14.99

8

4,564

15.76

9

1,753

6.05

10 （high）

997

3.44

Total

28,962100

Table A2: Description statistics
Mean

Standard deviation

Age

53.96772

14.66297

Spouse dummy

0.751122

0.43237

Regular employee dummy

0.322565

0.467466

38

Non-permanent employee dummy

0.213315

0.409655

Self-employed person dummy

0.147898

0.355005

Homeowner dummy

0.816463

0.387113

Psychosomatic symptom score11.31427

6.436945

Unemployment dummy

0.019123

0.13696

Volunteer participation rate

0.0776902

0.0471904

Equivalent household income

263.6326

210.1775

Individual income

310.4621

310.0594

Life satisfaction

5.856433

2.112893

39

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